https://github.com/igorjakus/neurohackathon
Epileptic Seizure Detection System for NeuroHackathon 2024 engineered with Igor Jakus, Hubert Berlicki, Kyrylo Goroshenko and Lidia Podoluk
https://github.com/igorjakus/neurohackathon
bci brain-computer-interface hackathon healthcare machine-learning neuroscience seizure-detection seizure-prediction
Last synced: 3 months ago
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Epileptic Seizure Detection System for NeuroHackathon 2024 engineered with Igor Jakus, Hubert Berlicki, Kyrylo Goroshenko and Lidia Podoluk
- Host: GitHub
- URL: https://github.com/igorjakus/neurohackathon
- Owner: igorjakus
- Created: 2024-11-16T12:03:18.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-02-07T18:12:11.000Z (3 months ago)
- Last Synced: 2025-02-07T19:19:59.469Z (3 months ago)
- Topics: bci, brain-computer-interface, hackathon, healthcare, machine-learning, neuroscience, seizure-detection, seizure-prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 18.7 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# BCI-Based Epileptic Seizure Detection and Warning System
## Project Overview
Originally created during Neurohackathon 2024, later enhanced and optimized as part of the Machine Learning Course.## Idea
This project is a Brain-Computer Interface (BCI) system designed to detect and warn users of the potential risk of epileptic seizures. Using advanced signal processing techniques and machine learning algorithms, the system analyzes EEG (Electroencephalography) data to identify patterns associated with seizure onset. The goal is to provide timely alerts, enabling individuals with epilepsy and their relatives to take preventive measures and improve their quality of life.## Features
- Predicts and detects epileptic seizures using real-time EEG data with a BCI.
- Provides timely alerts to users or their close ones.
- Achieved 99.75% accuracy by experimenting with various models and techniques: KNN, SVM, Logistic Regression, RNN, Dynamic Time Warping, Random Forest, Decision Tree, XGBoost, and Naive Bayes.## Team
- Igor Jakus
- Hubert Berlicki
- Kyrylo Goroshenko
- Lidia Podoluk## Resources
- Detailed presentation: `presentation.pdf`
- [Dataset and research paper](https://www.ukbonn.de/epileptologie/arbeitsgruppen/ag-lehnertz-neurophysik/downloads/)